Iranian Journal of Medical Sciences
Iranian Journal of Medical Sciences

Identification of Key Modules, Hub Genes, and Hub Proteins Associated with Glioblastoma Using Weighted Gene Co-expression Network Analysis and Proteomic Analysis

Articles InPress

Document Type : Original Article(s)

Authors

Nasrin Karimi 1
Atieh Tavakoli 1
Hosna Hesanmanesh 2
Mohadese Valizadeh 3, 4
Hamidreza Kheiri Manjili 5
Maryam Yousefi 6

1 Department of Biology, Damghan Branch, Islamic Azad University, Damghan, Iran

2 Department of Biology, Ahar Branch, Islamic Azad University, East Azerbaijan, Iran

3 Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

4 Men’s Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

5 Department of Pharmaceutical Nanotechnology, School of Pharmacy, Zanjan University of Medical Sciences, Zanjan, Iran

6 Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran

10.30476/ijms.2026.108885.4401
Abstract
Background: Glioblastoma (GBM) is the most aggressive primary brain tumor with a poor prognosis and limited effective therapeutic options. This study aimed to identify key genes and proteins involved in GBM progression and recurrence using integrated weighted gene co-expression network analysis (WGCNA) and proteomic analyses.
Methods: Raw data from nine datasets (GSE100675, GSE111260, GSE90598, GSE58399, GSE50161, GSE4290, GSE108474, GSE134470, and GSE103227) were used for WGCNA of GBM versus control, and the series matrix of GSE62153 was used for WGCNA of Recurrence versus Primary GBM. Transcriptomic data were analyzed in R software (version 4.x) using the limma, sva, and WGCNA packages. Differentially expressed genes (DEGs) were identified using the moderated t test with Benjamini–Hochberg False Discovery Rate (FDR) correction (adjusted P<0.05). Common genes between the selected modules and DEGs were identified. Proteomic data (ProteomeXchange PXD035867) were analyzed using MaxQuant and Perseus, and group comparisons were performed using the two-sample Student’s t test with FDR=0.05. Enrichment analyses determined shared pathways. All datasets were retrieved from public databases up to 2023, and the study was conducted as a bioinformatics analysis.
Results: Three WGCNA modules (turquoise, blue, and magenta) were identified in the GBM versus control (G/C) comparison, whereas a single module (blue) was identified in the recurrent versus primary GBMs (R/PG) comparison. The common genes between DEGs (G/C), DEGs (R/PG), WGCNA (G/C), and WGCNA (R/PG) (blue module) were aldehyde dehydrogenase 2 (ALDH2), arrestin beta 1 (ARRB1), and tumor protein p63 regulated 1-like (TPRG1L). DEG analysis demonstrated that ARRB1, ALDH2, and TPRG1L genes were downregulated in GBM compared to controls. Proteomic analysis identified three clusters, with fibrinogen gamma chain (FGG) and succinate-CoA ligase GDP-forming beta subunit (SUCLG2) as proteins related to ALDH2 and ARRB1.
Conclusion: Integrated transcriptomic and proteomic analyses identified ALDH2, ARRB1, and TPRG1L as key genes associated with glioblastoma progression, suggesting that these molecules may serve as potential biomarkers or therapeutic targets for GBM.

Highlights

Nasrin Karimi (Google Scholar)
Maryam Yousefi (Google Scholar)

Keywords

Glioblastoma
Gene expression profiling
Proteomics
Biomarkers
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Iranian Journal of Medical Sciences

Articles InPress, Corrected Proof
Available Online from 19 October 2026


  • Received 30 September 2025
  • Revised 20 February 2026
  • Accepted 24 April 2026

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